Source-linked AI summary
Environment Edits as a Corrective Signal for Reward Learning
Isaac Ngui, Courtney McBeth, Nancy M. Amato
TL;DR
Robot corrections can change multiple features and cannot express some preferences about object attributes or relations. FERRET combines robot corrections with environment edits made while robot motion is fixed, using relational and graph models. The experiments show that environment traces can disambiguate some preferences that robot corrections alone cannot, while model performance differs across scenarios.
Problem
Robot corrections can confound multiple feature changes and cannot express preferences that depend on object attributes or relations between objects.
Source: §1 · p. 1Method
FERRET learns preference features from robot and environment traces using hand-built relational features or graph models that compute relations from scene geometry.
Source: §1 · p. 2, §3.2 · p. 3Results
Environment traces let the Relational model learn a laptop-related feature in an ambiguous scenario where robot corrections failed, while graph models often learned wider high-feature regions than ground truth.
Source: §4.3 · p. 6, §4.2 · p. 6, §5 · p. 6Takeaways & Limitations
The Relational model is consistent across trace types, while graph models can capture attribute-dependent preferences but are prone to over-wide feature expression.
Source: §5 · p. 6FERL does not take object attributes as input and learned the same feature for open- and closed-cup conditions.
Source: §4.4 · p. 6
Abstract
from arXiv · showhide
Robot corrections allow people to adjust a robot's behavior, but a single correction changes many features. Also, preferences about object attributes and relationships between objects cannot be expressed by moving the robot. We propose environment edits as a complementary form of feedback. The robot's motion is held constant as the human edits the scene, so the only explanation for the edit is the moved object. We introduce Feature Learning from Environmental and Robot-Relational Edit Traces (FERRET), which learns preferences from both robot and environment corrections. We show that environment edits are able to disambiguate features and learn preferences that robot corrections alone cannot.
1 Introduction
Robot corrections can reveal motion preferences but confound multiple feature changes and cannot express some object or relationship preferences. FERRET pairs robot corrections with scene edits made while the robot’s motion is fixed, and explores relational and graph models for learning from both trace types.
1 Introduction
- Robot corrections can identify motion preferences such as speed or posture, and feature traces extend feedback to features the robot does not already know [4].The human moves the robot along a trace from strong to weak expression of the feature.Source: §1 · p. 1
- Moving the robot can change several candidate features at once, while some preferences—such as keeping a cup away from a laptop—cannot be expressed through robot motion.A correction may therefore be fit while the learner attributes it to the wrong feature.Source: §1 · p. 1
- With scene edits, the robot’s motion stays fixed, so the moved object is the explanation for the edit; scene and robot corrections capture complementary preferences.Scene edits capture preferences about objects, their attributes, and relations, while robot corrections capture preferences about robot motion.Source: §1 · pp. 1–2
- FERRET learns preference features from robot or environment traces using either hand-built scene relations or a graph model that learns relations within the network.The paper compares these architectures with each other and existing work.Source: §1 · p. 2
2 Related Work
Prior correction-learning methods largely treat feedback as displacement of the robot, while single corrections can change multiple features. FERL learns unknown features from robot traces; this work adds scene displacement as another way to express preferences.
2.1 Learning from Robot Trajectory Corrections
- Earlier correction methods treat pushes or other feedback as evidence about reward, with approaches addressing uncertainty, robustness, or combining corrections with other preference signals [3], [5], [6],.These methods share the assumption that feedback is the robot’s displacement; this work adds displacement of the scene.Source: §2.1 · p. 2
2.2 Ambiguity in Corrections
- A single robot correction changes multiple features, making it ambiguous which feature the human intended to convey.Scene edits hold robot motion fixed, so the moved object is the explanation for the feature conveyed.Source: §2.2 · p. 2
2.3 Feature Expansive Reward Learning
- FERL learns previously unknown features from a few robot trajectories that move from high to low feature expression [4].Its Bradley-Terry-based loss encourages feature values to decrease along each trace and makes trace endpoints indistinguishable.Source: §2.3 · p. 2
2 Related Work
- This work builds on FERL [4] by adding feature traces in which people move objects in the environment.The related-work discussion frames this addition against methods that assume feedback is robot displacement.Source: §2.3 · p. 2, §2.1 · p. 2
3 Feature Learning from Environmental and Robot Relational Edit Traces
FERRET learns from robot and environment feature traces, collected in XR, using relational or graph-based models. The relational model encodes end-effector–object relations by hand, while graph models compute relations over scene geometry.
3.1 Collecting Feature Traces in Extended Reality
- Extended reality (XR) supports feature-trace collection without risk of a virtual robot causing real damage in high-cost states, and lets users alter object characteristics to express relevant attributes.For example, a user can change a cup from empty to full.Source: §3.1 · p. 3, Figure 1 · p. 3
3.2 Learning from Environmental and Robot Edits
- FERRET augments FERL’s robot-state and object-position traces with relational and graph models for learning from robot or environment edits.The relational model uses end-effector–object relation vectors; the graph model moves relational computation into a scene graph rather than hand-designed inputs.Source: §3.2 · p. 3
3.2.1 Relational Model
- The relational model represents each state as end-effector–object relations containing relative position, distance, footprint distance, object type and attributes, and carried-object attributes.These values are manually specified; object embeddings are noted as a possible extension.Source: §3.2.1 · p. 3
- Relative positions make moving an object equivalent to moving the end effector in the opposite direction, allowing the relational model to learn from either robot or scene corrections.The feature is permutation-invariant and does not depend on the number of objects.Source: §3.2.1 · p. 3
3.2.2 Graph Model
- The graph model represents scenes with end-effector, object-center, and object-corner nodes, using edge features and message passing to compute relations within the network.Object-object edges allow it to represent configurations such as an open cup beside a laptop, unlike the relational model’s end-effector–object pairs.Source: §3.2.2 · p. 3
- Figure 2 depicts ground-truth and learned avoid-laptop features, while Figure 3 reports MSE, PR-AUC, and Spearman correlation for robot and environment traces.The figure captions identify the plotted content and trace types.Source: Figure 2 · p. 4, Figure 3 · p. 4
- Graph and SoftGraph differ in position scaling and message-passing rounds, trading displacement signal strength and object interaction against bounded inputs and weaker pairwise reasoning.Graph uses fixed scaling and two rounds; SoftGraph bounds edge inputs by one and uses a single round, limiting it to pairwise reasoning.Source: §3.2.2 · p. 4
4 Experimental Results
Across three scenarios, the experiments compare FERL and FERRET architectures using robot and environmental feature traces. Environment traces help the Relational model disambiguate which object matters, while graph models show promise for attribute-dependent preferences but can express features too broadly.
4.1 Experimental Setup
- The evaluation covers three scenarios, scales the number of author-generated feature traces, and reports five-seed means for MSE, PR-AUC, and Spearman correlation.The scenarios test laptop avoidance, disambiguation from a nearby cup, and avoidance conditional on carrying an open rather than closed cup.Source: §4.1 · pp. 4, 6
4.2 Learning Object Proximity Features
- For environmental traces, the Relational model learned consistently across trace types; graph models expressed high feature values over a wider area than ground truth.Robot traces led all models to converge, though the Graph model required more data; FERL’s environmental input lacked enough scene information to learn feature values, despite localized laptop-region performance on PR-AUC and Spearman correlation.Source: §4.2 · p. 6
4.3 Disambiguating Spurious Correlations
- With robot corrections, no model identified the laptop as relevant; with environment traces, the Relational model learned a feature similar to ground truth.Graph models expressed high feature values across a wider area than ground truth, while FERL could not associate the feature with the laptop.Source: §4.3 · p. 6
4.4 Attribute-Specific Features
- For open- versus closed-cup conditions, FERL learned the same feature, while graph models recovered close to zero error for the closed-cup condition.The authors suggest graph models may better learn feature dependence on object attributes.Source: §4.4 · p. 6
5 Conclusion
The conclusion presents FERRET as learning features from robot and environmental traces, with environment edits addressing feature ambiguity that FERL cannot resolve. The Relational model is consistent across trace types, while graph models better capture attribute dependence but may express features too broadly.
5 Conclusion
- FERL cannot learn features requiring environmental information to disambiguate spurious correlations, while FERRET learns from both robot and environment traces.The Relational model’s hand-built inputs are consistent across trace types; graph models learn from scene geometry and better capture attribute-dependent preferences but can express features too broadly.Source: §5 · p. 6
- The authors identify learned object embeddings and boundary-of-acceptability edits as future directions for improving feature and weight learning.They also plan a user study of how people choose between correcting the robot and the scene.Source: §5 · p. 6
- The conclusion’s practical takeaway is that the Relational and graph architectures have different strengths: trace-type consistency versus attribute dependence.The graph models’ wider feature expression is a stated trade-off.Source: §5 · p. 6